a Traffic data Visualization and Annotation tool - version 1.1 as published in the Journal of Open-Source Software

US Automatic Traffic Recorder Stations Data

Vehicle Traffic Counts and Locations at US ATR Stations

By Homeland Infrastructure Foundation [source]

About this dataset

This comprehensive dataset records important information about Automatic Traffic Recorder (ATR) Stations located across the United States. ATR stations play a crucial role in traffic management and planning by continuously monitoring and counting the number of vehicles passing through each station.

The data contained in this dataset has been meticulously gathered from station description files supplied by the Federal Highway Administration (FHWA) for both Weigh-in-Motion (WIM) devices and Automatic Traffic Recorders. In addition to this, location referencing data was sourced from the National Highway Planning Network version 4.0 as well as individual State offices of Transportation.

The database includes essential attributes such as a unique identifier for each ATR station, indicated by 'STTNKEY'. It also indicates if a site is part of the National Highway System, denoted under 'NHS'. Other key aspects recorded include specific locations generally named after streets or highways under 'LOCATION', along with relevant comments providing additional context in 'COMMENT'.

Perhaps one of the most critical factors noted in this data set would be traffic volume at each location, measured by Annual Average Daily Traffic ('AADT'). This metric represents total vehicle flow on roads or highways for a year divided over 365 days — an essential numeric analyst's often call upon when making traffic-related predictions or decisions.

Location coordinates incorporating longitude and latitude measurements of every ATR station are documented clearly — aiding geospatial analysis. Furthermore, X and Y coordinates correspond to these locations facilitating accurate map plotting.

Additional information contained also includes postal codes labeled as 'STPOSTAL' where stations are located with respective state FIPS codes indicated under ‘STFIPS’. County specific FIPS code are documented within ‘CTFIPS’. Versioning information helps users track versions ensuring they work off latest datasets with temporal geographic attribute updates captured via ‘YEAR_GEO’.

Reference Source: Click Here

How to use the dataset

Introduction

Diving into the data

The dataset comprises a collection of attributes for each station such as its location details (latitude, longitude), AADT or The Annual Average Daily Traffic amount, classification of road where it's located etc. Additionally, there is information related to when was this geographical information last updated.

Understanding Columns

Here's what primary columns represent: - Sttnkey: A unique identifier for each station. - NHS: Indicates if the station is part of national highway system. - Location: Describes specific location of a station with street or highway name. - Comment: Any additional remarks related to that station. - Longitude,Latitude: Geographic coordinates. - STPostal: The postal code where a given station resides. - menu 4 dots indicates show more items** - ADT: Annual Average Daily Traffic count indicating average volume of vehicles passing through that route annually divided by 365 days - Year_GEO: The year when geographic information was last updated - can provide insight into recency or timeliness of recorded attribute values - Fclass: Road classification i.e interstate,dis,e tc., providing context about type/stature/importance or natureof theroad on whichstationlies 11.Stfips,Ctfips- FIPS codes representing state,county respectively

Using this information

Given its structure and contents,thisdatasetisveryusefulforanumberofpurposes:

1.Urban Planning & InfrastructureDevelopment Understanding traffic flows and volumes can be instrumental in deciding where to build new infrastructure or improve existing ones. Planners can identify high traffic areas needing more robust facilities.

2.Traffic Management & Policies Analysing chronological changes and patterns of traffic volume, local transportation departments can plan out strategic time-based policies for congestion management.

3.Residential/CommercialRealEstateDevelopment Real estate developers can use this data to assess the appeal of a location based on its accessibility i.e whether it sits on high-frequency route or is located in more peaceful, low-traffic areas etc

4.Environmental AnalysisResearch: Re...

This map contains a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from TomTom (www.tomtom.com). Historical traffic is based on the average of observed speeds over the past year. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes. The map also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

The Smart Mobility and Traffic Optimization Dataset integrates data from cyber-physical networks (CPNs) and social networks (SNs) to improve traffic management and smart mobility solutions. By combining real-time traffic patterns, vehicle telemetry, ride-sharing demand, public transport efficiency, social media sentiment, and environmental factors, this dataset provides a comprehensive foundation for optimizing urban mobility.

Designed to support machine learning models, the dataset enables accurate predictions of traffic congestion, mobility optimization, and smart city planning. It incorporates key metrics such as vehicle density, road occupancy, weather conditions, social media feedback, and emissions data to generate actionable insights.

Key Features: Traffic Data: Includes vehicle count, speed, road occupancy, and traffic light status, offering a granular view of real-time traffic conditions. Weather & Accidents: Integrates weather conditions and accident reports to assess their impact on congestion levels. Social Network Sentiment: Analyzes public opinions and complaints about mobility and congestion, extracted from social media platforms. Smart Mobility Factors: Examines ride-sharing demand, parking availability, and public transport delays, aiding in urban mobility planning. Environmental Impact: Monitors CO₂ emissions and pollution levels, ensuring eco-friendly traffic optimization. Target Variable: The dataset categorizes traffic congestion levels into three main groups: Low, Medium, or High, based on real-time traffic density, speed, and road occupancy.

This dataset is an essential resource for urban planners, smart city developers, and AI researchers, empowering them to create intelligent mobility solutions that reduce congestion, enhance efficiency, and improve overall urban sustainability.

The size and share of this market is categorized based on Data Collection (Mobile Data, GPS Data, Sensor Data, Crowdsourced Data, IoT Data) and Data Processing (Real-Time Analytics, Data Integration, Data Visualization, Machine Learning Algorithms, Big Data Processing) and End-User Applications (Traffic Management Systems, Navigation Applications, Fleet Management, Smart City Solutions, Logistics and Supply Chain Management) and Deployment Mode (Cloud-Based Solutions, On-Premises Solutions) and Industry Verticals (Transportation, Retail, Telecommunications, Government, Automotive) and geographical regions (North America, Europe, Asia-Pacific, South America, Middle-East and Africa).

Update NotesMar 16 2024, remove spaces in the file and folder names.Mar 31 2024, delete the underscore in the city names with a space (such as San Francisco) in the '02_TransCAD_results' folder to ensure correct data loading by TransCAD (software version: 9.0).Aug 31 2024, add the 'cityname_link_LinkFlows.csv' file in the '02_TransCAD_results' folder to match the link from input data and the link from TransCAD results (LinkFlows) with the same Link_ID.IntroductionThis is a unified and validated traffic dataset for 20 US cities. There are 3 folders for each city.01 Input datathe initial network data obtained from OpenStreetMap (OSM)the visualization of the OSM dataprocessed node / link / od data02 TransCAD results (software version: 9.0)cityname.dbd : geographical network database of the city supported by TransCAD (version 9.0)cityname_link.shp / cityname_node.shp : network data supported by GIS software, which can be imported into TransCAD manually. Then the corresponding '.dbd' file can be generated for TransCAD with a version lower than 9.0od.mtx : OD matrix supported by TransCADLinkFlows.bin / LinkFlows.csv : traffic assignment results by TransCADcityname_link_LinkFlows.csv: the input link attributes with the traffic assignment results by TransCADShortestPath.mtx / ue_travel_time.csv : the traval time (min) between OD pairs by TransCAD03 AequilibraE results (software version: 0.9.3)cityname.shp : shapefile network data of the city support by QGIS or other GIS softwareod_demand.aem : OD matrix supported by AequilibraEnetwork.csv : the network file used for traffic assignment in AequilibraEassignment_result.csv : traffic assignment results by AequilibraEPublicationXu, X., Zheng, Z., Hu, Z. et al. (2024). A unified dataset for the city-scale traffic assignment model in 20 U.S. cities. Sci Data 11, 325. https://doi.org/10.1038/s41597-024-03149-8Usage NotesIf you use this dataset in your research or any other work, please cite both the dataset and paper above.A brief introduction about how to use this dataset can be found in GitHub. More detailed illustration for compiling the traffic dataset on AequilibraE can be referred to GitHub code or Colab code.ContactIf you have any inquiries, please contact Xiaotong Xu (email: kid-a.xu@connect.polyu.hk).

Snapshot img

Intelligent Traffic Management Market Size 2025-2029

The intelligent traffic management market size is forecast to increase by USD 24.01 billion at a CAGR of 14.8% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for advanced, AI-based traffic solutions. This demand is driven by the escalating number of vehicles on the road and the resulting need for more efficient and effective traffic management systems. However, the market faces challenges as well. The lack of skilled professionals in government traffic organizations poses a significant barrier to the implementation and maintenance of these complex systems. Despite these challenges, the market presents numerous opportunities for companies seeking to capitalize on the growing demand for intelligent traffic management solutions.
Green traffic lights, on-demand transportation, and shared mobility services are also gaining popularity, contributing to the evolution of the traffic management infrastructure. Strategic partnerships, collaborations, and investments in research and development are key strategies for companies looking to stay competitive in this dynamic market. By addressing the skills gap and continuing to innovate, companies can help ensure the successful implementation and adoption of intelligent traffic management systems, ultimately improving traffic flow, reducing congestion, and enhancing public safety.

What will be the Size of the Intelligent Traffic Management Market during the forecast period?

Request Free Sample

The market in the United States is experiencing significant growth, driven by the increasing demand for next-generation traffic management solutions. Traffic safety technologies, such as real-time traffic information, dynamic traffic routing, and pedestrian detection systems, are becoming essential components of the smart mobility ecosystem. The integration of traffic data acquisition and data-driven traffic management is revolutionizing urban traffic management, leading to road safety improvement and sustainable transportation. Traffic management innovation continues to shape the industry, with a focus on transportation network analysis, traffic data visualization, and traffic congestion mitigation.
Intelligent parking management and traffic incident detection are essential components of the market, ensuring efficient and safe traffic flow. The market is also witnessing the emergence of mobility-as-a-service (MaaS) platforms, which are transforming the way people move around cities. The market's growth is further fueled by the development of traffic management standards and the increasing adoption of data-driven approaches. The trend towards sustainable traffic management is also influencing the market, with a focus on reducing carbon emissions and improving overall transportation efficiency. In summary, the market in the United States is a dynamic and rapidly evolving industry, driven by the demand for next-generation traffic management solutions and the integration of data-driven approaches. The market's growth is underpinned by the need for improved traffic operations management, sustainable transportation, and the development of a smart mobility ecosystem.

How is the Intelligent Traffic Management Industry segmented?

The intelligent traffic management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Solution

  Traffic monitoring system
  Traffic signal control system
  Traffic enforcement camera
  Integrated corridor management
  Others


Component

  Surveillance cameras
  Video walls
  Traffic controllers and signals
  Others


End-user

  Government authorities
  Transport agencies
  Commercial


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America



  Middle East and Africa

By Solution Insights

The traffic monitoring system segment is estimated to witness significant growth during the forecast period. The market is witnessing significant advancements, particularly in the Traffic Monitoring Systems segment. By 2029, this segment is expected to evolve substantially, integrating advanced sensor technologies, video analytics, and real-time data processing frameworks. These systems will shift from reactive to proactive approaches, utilizing predictive analytics algorithms to anticipate congestion patterns and optimize signal timings dynamically. IoT-enabled devices and edge computing architectures will facilitate faster data transmission and localized decision-making, minimizing latency in traffic management operations. Furthermore, multimodal transportation data, including

Historical road traffic data from permanent sensors from 2010 to year A-1.

This dataset corresponds to the history of that of the current year Road counting - Traffic data from permanent sensors

On the Parisian network, traffic is measured mainly through electromagnetic loops implanted in the road.

The data is produced by the Department of Roads and Travel - Service des Déplacements - Poste Central d'Exploitation Lutèce.

The data and associated visualizations (Table, Map and Dataviz) are raw without any interpretation or analysis. They show the data as it is published daily.

They give an overview of the occupancy rate and throughput on more than 3000 track sections. By themselves, they do not make it possible to characterize the complexity of traffic in Paris.

< b>

Two types of data are thus elaborated:

• the occupancy rate, which corresponds to the presence time of vehicles on the loop as a percentage of a fixed time interval (one hour for the data provided). Thus, a 25% occupancy rate over one hour means that vehicles have been present on the loop for 15 minutes. The rate provides information on road congestion. The layout of the loops is thought out in such a way as to be able to deduce, from a specific measurement, the state of the traffic on an arc.
• bitrate is the number of vehicles that passed the counting point during a fixed time interval (one hour for the provided data).

The timestamp is performed at the end of the production period based on the Europe Time Zone Paris - Berlin UTC +1

For example, the timestamp "2019-01-01 01:00:00" denotes the period from January 1, 2019 at 00:00 to January 1, 2019 at 01:00.

Thus, the coupled observation at one point of the occupancy rate and the throughput makes it possible to characterize the traffic. This is one of the foundations of traffic engineering, and is referred to as the "fundamental diagram".

A flow can correspond to two traffic situations: fluid or saturated, hence the need for the occupancy rate. For example: over an hour, a flow of 100 vehicles per hour on a usually very busy axis can occur at night (fluid traffic) or during rush hour (saturated traffic).

Paris network equipment:

The main axes of the City of Paris are equipped with vehicle counting stations and measurement of the occupancy rate, for the purposes of both traffic regulation and public transport, d information to users (dissemination on the Sytadin site), and study.

There are two types of stations on the network: stations measuring the occupancy rate only, and stations both measuring the rate and counting vehicles.< /p>

The rate measurement stations are set up very regularly: they allow detailed knowledge of traffic conditions.

Debit stations are less numerous, and generally located between major intersections. Indeed, the flow is generally preserved on a section between two large intersections.

The repository:

The repository is available on this dataset Road count - Geographical reference with the following characteristics: </ span>

• Encoding: UTF-8
• Projection: EPSG:2154 (Lambert 93 – RGF93)

Attribute fields are: see data model below and attached notice.

Traffic volume counts collected by DOT for New York Metropolitan Transportation Council (NYMTC) to validate the New York Best Practice Model (NYBPM).

Dataset

This dataset was created by Nilay Desmukh

Released under Other (specified in description)

Contents

Traffic Count Viewer is an online mapping application, which users can use to explore traffic count reports in different locations within the Delaware Valley, including Philadelphia. Users search by location (address, city, zip code, or place name) to view point features on the interactive mapping visualization of traffic records. Clicking on a point of interest or grouping multiple points on the map yields traffic count information tables, which includes: Date of Counnt ; DVRPC File # ; Type ; Annual Average Daily Traffic (AADT) ; Municipality ; Route Number ; Road Name ; Count Direction ; and From/To Locations, as well as a link to the detailed (hourly) report. Data tables are exportable as .CSV and detailed reports are available for export in multiple formats (including basic .doc and .rtf outputs.) Traffic count data is collected by the Delaware Valley Regional Planning Commission and other agencies.

A file with preprocessed data of traffic flow simulation and GraphML file describing the simulated area of the traffic network. This data is used for the example visualization of traffic flow by IT4Innovations/FlowMapFrame package.

The vehicle behavior on the Hanshin Expressway, the urban expressway in Japan, in 0.1 second intervals, for all vehicles driving in target sections that span multiple kilometers are turned into trajectory data over a long period of time. The vehicle trajectory database is intended to realize a more safe, secure and comfortable drive for all highway users. The data is made targeting congestion bottlenecks using image sensing technology. The data is obtained by cameras installed on some of the light poles of the Hanshin Expressway to observe all vehicles in target sections at a 0.1 second interval. On top of that, other data such as vehicle length and other vehicle attributes together with location data at 0.1 second intervals and corresponding road surface information (longitudinal and cross slopes, road curvature, etc.) are combined.
The data covers roughly 100% of each vehicle’s trajectory for all vehicles in the target sections. Thus, the trajectories for each vehicle are generally continuous in the target section. The data can be used for various analyses and studies such as estimation of traffic conditions, understanding the influence of vehicle behavior, creation of scenarios for the various actual traffic conditions, visualization of actual traffic conditions, reinterpretation and rationalization of installed sensors and so on. The data can also be expected to contribute to improve road traffic services. As of September 2018, five one-hour traffic datasets obtained at the about 2 km section of the Hanshin Expressway, can be utilized.

This dataset captures key factors influencing traffic accidents in both urban and rural areas it provides detailed information about environmental infrastructural and behavioral variables that are crucial for understanding the dynamics of road safety with a focus on 8756 observations it covers a wide range of scenarios from dense urban intersections to quieter rural roads the number of recorded traffic accidents ranges from minor incidents to significant collisions the traffic fine amount represents the average amount of traffic fines in thousands of USD in the observed area linked to enforcement efforts and driver behavior traffic density is represented by a score indicating the volume of vehicles in the area on a scale from 0 low to 10 high the proportion of traffic lights in the area highlights intersections with varying levels of control pavement quality is rated from 0 to 5 with higher values indicating better infrastructure there is a binary indicator showing whether the area is urban 1 or rural 0 the dataset also captures the typical speed of vehicles in kilometers per hour representing driving conditions rain intensity is measured on a scale from 0 no rain to 3 heavy rain emphasizing the role of weather in accidents the estimated number of vehicles in thousands present in the area during the observation is also included the dataset uses a 24-hour format from 0 to 24 to capture temporal patterns in accident occurrences this dataset is designed for traffic safety analysis urban planning and infrastructure improvement predictive modeling to identify high-risk conditions and prevent accidents and policymaking to enhance road safety and reduce traffic-related incidents researchers urban planners and policymakers can analyze trends to identify temporal and spatial patterns of accidents develop machine learning models to predict accident hotspots prioritize areas needing better pavement quality or traffic control and understand the role of weather speed and traffic density in accident rates the dataset is entirely fictitious and has been created for educational and illustrative purposes only it does not represent real-world data and should not be used for decision-making or policy implementation without validation against actual data sources it is intended to demonstrate analytical methods and modeling techniques in the context of traffic safety.

Explore Traffic jam through data from visualizations to datasets, all based on diverse sources.

The global real-time traffic information systems market is experiencing robust growth, driven by increasing urbanization, escalating traffic congestion in major cities, and the growing adoption of smart city initiatives. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated $45 billion by 2033. This significant expansion is fueled by several key factors, including the rising demand for efficient transportation management, advancements in sensor technologies (like LiDAR and radar), the proliferation of connected vehicles, and the increasing availability of high-speed internet connectivity. Furthermore, government initiatives aimed at improving transportation infrastructure and reducing traffic-related accidents are significantly contributing to market growth. The integration of real-time traffic data with other smart city applications, such as public transportation systems and emergency response services, further enhances the value proposition of these systems. The market is segmented by type (software, hardware, and services) and application (urban traffic, inter-urban traffic, info-mobility, public transport, freeway management, and others). The software segment currently holds a significant market share due to the increasing demand for advanced analytics and data visualization capabilities. Geographically, North America and Europe are leading the market, driven by early adoption of advanced technologies and well-established transportation infrastructure. However, the Asia-Pacific region is expected to witness the fastest growth over the forecast period, fueled by rapid urbanization and significant investments in smart city projects across countries like China and India. Despite the positive outlook, challenges such as high initial investment costs, data security concerns, and the need for robust data infrastructure in developing countries could potentially hinder market growth to some extent.

Размер и доля сегментированы по Data Collection (Mobile Data, GPS Data, Sensor Data, Crowdsourced Data, IoT Data) and Data Processing (Real-Time Analytics, Data Integration, Data Visualization, Machine Learning Algorithms, Big Data Processing) and End-User Applications (Traffic Management Systems, Navigation Applications, Fleet Management, Smart City Solutions, Logistics and Supply Chain Management) and Deployment Mode (Cloud-Based Solutions, On-Premises Solutions) and Industry Verticals (Transportation, Retail, Telecommunications, Government, Automotive) and регионам (Северная Америка, Европа, Азиатско-Тихоокеанский регион, Южная Америка, Ближний Восток и Африка)

The data includes the up-to-date information on traffic-relevant events of the State Reporting Office (LMS) NRW as an individual report upon occurrence. In addition to the safety-relevant traffic information (e.g. wrong-way driver, unsecured accident site, smoothness) in accordance with the Framework Directive for the Traffic Warning Service (RVWD), the reporting inventory also includes information on congestion on the motorways in North Rhine-Westphalia. Specifically, the publication includes the following information (if available): -Reference room: North Rhine-Westphalia -Validity period of the traffic information -Type of information -Sections concerned -If necessary: Additional information The data is constantly updated.

Long-term Pavement performance, construction, traffic, and environmental data for more than 2500 pavement sections in the United States and Canada. More than a dozen experimental designs address specially constructed and existing asphalt and concrete pavements, and maintenance and rehabilitation strategies. Data collection has been on-going since 1990. About one third of the pavement sections are still under study. New warm-mix asphalt concrete pavement overlay sections are currently being recruited and constructed.

Analysis of ‘Daily traffic indicators France and Regions, COVID-19 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/615eed271fd73f6703435810 on 12 January 2022.

--- Dataset description provided by original source is as follows ---

Daily Road Traffic Indicators make it possible to compare the road traffic of all vehicles (ITV or All Vehicles Index) or only heavy goods vehicles (IPL or Index Poids Lourds) with a situation before the COVID-19 crisis.

# # Calculation method

They are constructed by comparing current traffic with pre-crisis traffic based on average daily flow rates between 13 January and 2 February 2020.

This period was chosen in order to avoid the effects of school holidays.

‘0’ therefore represents a ‘pre-crisis’ situation and the curves directly give the observed falls (negative index) or traffic increases (positive index).

Traffic indicators at the level of France and regions are calculated on the basis of traffic data of more than 1200 counting stations spread across the unlicensed national road network and 450 stations spread across the national road network as a whole.

# # Dictionary of variables

— ‘Zone’: Geographical area (e.g. Bourgogne-Franche-Comté, France, etc.) — ‘ITV’: All Vehicles Index (between -1 and 1) — ‘IPL’: Weight Lourds index (between -1 and 1) — ‘MGL_ITV’: Rolling average All vehicles (between -1 and 1) — ‘MGL_IPL’: Rolling Average Weight Lourds (between -1 and 1)

# Data-visualisation

image of DataViz

The [dataviz.cerema.fr/trafic-routier] platform (https://dataviz.cerema.fr/trafic-routier) allows you to explore, visualise and analyse the state of traffic in France, day after day

--- Original source retains full ownership of the source dataset ---